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cs.CV2025
Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection
Yuanting Fan, Jun Liu, Xiaochen Chen +5
Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-l…
cs.CV2025
DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
Jiawei Zhan, Jun Liu, Jinlong Peng +4
With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…
cs.CV2025
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Bin-Bin Gao, Yue Zhou, Jiangtao Yan +7
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studie…